std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
+ const int num = inputs[0][0];
CV_Assert(inputs.size() >= 3);
- CV_Assert(inputs[0][0] == inputs[1][0]);
+ CV_Assert(num == inputs[1][0]);
int numPriors = inputs[2][2] / 4;
CV_Assert((numPriors * _numLocClasses * 4) == total(inputs[0], 1));
// num() and channels() are 1.
// Since the number of bboxes to be kept is unknown before nms, we manually
- // set it to maximal number of detections, [keep_top_k] parameter.
+ // set it to maximal number of detections, [keep_top_k] parameter multiplied by batch size.
// Each row is a 7 dimension std::vector, which stores
// [image_id, label, confidence, xmin, ymin, xmax, ymax]
- outputs.resize(1, shape(1, 1, _keepTopK, 7));
+ outputs.resize(1, shape(1, 1, _keepTopK * num, 7));
return false;
}
normAssertDetections(ref, out);
}
-typedef testing::TestWithParam<Target> Reproducibility_MobileNet_SSD;
+typedef testing::TestWithParam<tuple<Backend, Target> > Reproducibility_MobileNet_SSD;
TEST_P(Reproducibility_MobileNet_SSD, Accuracy)
{
const string proto = findDataFile("dnn/MobileNetSSD_deploy.prototxt", false);
const string model = findDataFile("dnn/MobileNetSSD_deploy.caffemodel", false);
Net net = readNetFromCaffe(proto, model);
- int targetId = GetParam();
- const float l1 = (targetId == DNN_TARGET_OPENCL_FP16) ? 1.5e-4 : 1e-5;
- const float lInf = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-4;
+ int backendId = get<0>(GetParam());
+ int targetId = get<1>(GetParam());
- net.setPreferableBackend(DNN_BACKEND_OPENCV);
+ net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat sample = imread(_tf("street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
- Mat out = net.forward();
+ Mat out = net.forward().clone();
- const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 4e-4 : 1e-5;
- const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16) ? 5e-3 : 1e-4;
+ const float scores_diff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.5e-2 : 1e-5;
+ const float boxes_iou_diff = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 6.3e-2 : 1e-4;
Mat ref = blobFromNPY(_tf("mobilenet_ssd_caffe_out.npy"));
- normAssertDetections(ref, out, "", 0.0, scores_diff, boxes_iou_diff);
+ normAssertDetections(ref, out, "", FLT_MIN, scores_diff, boxes_iou_diff);
// Check that detections aren't preserved.
inp.setTo(0.0f);
net.setInput(inp);
- out = net.forward();
- out = out.reshape(1, out.total() / 7);
+ Mat zerosOut = net.forward();
+ zerosOut = zerosOut.reshape(1, zerosOut.total() / 7);
- const int numDetections = out.rows;
+ const int numDetections = zerosOut.rows;
ASSERT_NE(numDetections, 0);
for (int i = 0; i < numDetections; ++i)
{
- float confidence = out.ptr<float>(i)[2];
+ float confidence = zerosOut.ptr<float>(i)[2];
ASSERT_EQ(confidence, 0);
}
+ // There is something wrong with Reshape layer in Myriad plugin and
+ // regression with DLIE/OCL_FP16 target.
+ if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+ {
+ if ((targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2) ||
+ targetId == DNN_TARGET_OPENCL_FP16)
+ return;
+ }
+
// Check batching mode.
- ref = ref.reshape(1, numDetections);
inp = blobFromImages(std::vector<Mat>(2, sample), 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
net.setInput(inp);
Mat outBatch = net.forward();
// Output blob has a shape 1x1x2Nx7 where N is a number of detection for
// a single sample in batch. The first numbers of detection vectors are batch id.
- outBatch = outBatch.reshape(1, outBatch.total() / 7);
- EXPECT_EQ(outBatch.rows, 2 * numDetections);
- normAssert(outBatch.rowRange(0, numDetections), ref, "", l1, lInf);
- normAssert(outBatch.rowRange(numDetections, 2 * numDetections).colRange(1, 7), ref.colRange(1, 7),
- "", l1, lInf);
+ // For Inference Engine backend there is -1 delimiter which points the end of detections.
+ const int numRealDetections = ref.size[2];
+ EXPECT_EQ(outBatch.size[2], 2 * numDetections);
+ out = out.reshape(1, numDetections).rowRange(0, numRealDetections);
+ outBatch = outBatch.reshape(1, 2 * numDetections);
+ for (int i = 0; i < 2; ++i)
+ {
+ Mat pred = outBatch.rowRange(i * numRealDetections, (i + 1) * numRealDetections);
+ EXPECT_EQ(countNonZero(pred.col(0) != i), 0);
+ normAssert(pred.colRange(1, 7), out.colRange(1, 7));
+ }
}
-INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD,
- Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16));
+INSTANTIATE_TEST_CASE_P(/**/, Reproducibility_MobileNet_SSD, dnnBackendsAndTargets());
typedef testing::TestWithParam<Target> Reproducibility_ResNet50;
TEST_P(Reproducibility_ResNet50, Accuracy)